metadata
datasets:
- suchintikasarkar/sentiment-analysis-for-mental-health
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
pipeline_tag: text-generation
tags:
- mental_health
- Meta-Llama-3.1-8B-Instruct
Llama-3.1-8B-Instruct-Mental-Health-Classification
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on an suchintikasarkar/sentiment-analysis-for-mental-health dataset.
Tutorial
Get started with the new Llama models and customize Llama-3.1-8B-It to predict various mental health disorders from the text by following the Fine-Tuning Llama 3.1 for Text Classification tutorial.
Use with Transformers
from transformers import AutoTokenizer,AutoModelForCausalLM,pipeline
import torch
model_id = "abdullahmazhar51/Llama-3.1-8B-Instruct-Mental-Health-Classification"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
return_dict=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
text = "I constantly worry about everything, even small things, and it's making it hard for me to focus on my work and enjoy life."
prompt = f"""Classify the text into Normal, Depression, Anxiety, Bipolar, and return the answer as the corresponding mental health disorder label.
text: {text}
label: """.strip()
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipe(prompt, max_new_tokens=2, do_sample=True, temperature=0.1)
print(outputs[0]["generated_text"].split("label: ")[-1].strip())
# Depression
Results
100%|██████████| 300/300 [03:24<00:00, 1.47it/s]
Accuracy: 0.913
Accuracy for label Normal: 0.972
Accuracy for label Depression: 0.913
Accuracy for label Anxiety: 0.667
Accuracy for label Bipolar: 0.800
Classification Report:
precision recall f1-score support
Normal 0.92 0.97 0.95 143
Depression 0.93 0.91 0.92 115
Anxiety 0.75 0.67 0.71 27
Bipolar 1.00 0.80 0.89 15
accuracy 0.91 300
macro avg 0.90 0.84 0.87 300
weighted avg 0.91 0.91 0.91 300
Confusion Matrix:
[[139 3 1 0]
[ 5 105 5 0]
[ 6 3 18 0]
[ 1 2 0 12]]